Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations2199
Missing cells73
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory458.1 KiB
Average record size in memory213.3 B

Variable types

Text2
Numeric11

Alerts

Freedom to make life choices is highly overall correlated with Happiness score and 1 other fieldsHigh correlation
Generosity is highly overall correlated with GenerositysHigh correlation
Generositys is highly overall correlated with GenerosityHigh correlation
Happiness score is highly overall correlated with Freedom to make life choices and 4 other fieldsHigh correlation
Healthy life expectancy at birth is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Log GDP per capita is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Positive affect is highly overall correlated with Freedom to make life choices and 1 other fieldsHigh correlation
Social support is highly overall correlated with Happiness score and 2 other fieldsHigh correlation
Generosity has 73 (3.3%) missing values Missing

Reproduction

Analysis started2025-03-18 08:14:34.229925
Analysis finished2025-03-18 08:14:48.521957
Duration14.29 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct165
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size140.3 KiB
2025-03-18T17:14:48.724841image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length25
Median length22
Mean length8.253297
Min length4

Characters and Unicode

Total characters18149
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
united 49
 
1.8%
china 43
 
1.6%
of 42
 
1.6%
south 37
 
1.4%
republic 22
 
0.8%
congo 22
 
0.8%
and 19
 
0.7%
chile 17
 
0.6%
bolivia 17
 
0.6%
bangladesh 17
 
0.6%
Other values (178) 2366
89.2%
2025-03-18T17:14:49.066645image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18149
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2882
15.9%
i 1588
 
8.7%
n 1484
 
8.2%
e 1209
 
6.7%
o 1029
 
5.7%
r 1000
 
5.5%
t 669
 
3.7%
l 657
 
3.6%
u 553
 
3.0%
s 540
 
3.0%
Other values (44) 6538
36.0%
Distinct165
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size129.0 KiB
2025-03-18T17:14:49.331496image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.992724
Min length2

Characters and Unicode

Total characters6581
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st rowAFG
2nd rowAFG
3rd rowAFG
4th rowAFG
5th rowAFG
ValueCountFrequency (%)
arg 17
 
0.8%
cri 17
 
0.8%
bra 17
 
0.8%
bol 17
 
0.8%
bgd 17
 
0.8%
col 17
 
0.8%
chl 17
 
0.8%
khm 17
 
0.8%
cmr 17
 
0.8%
can 17
 
0.8%
Other values (155) 2029
92.3%
2025-03-18T17:14:49.691298image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 536
 
8.1%
A 533
 
8.1%
N 486
 
7.4%
M 385
 
5.9%
E 384
 
5.8%
L 377
 
5.7%
S 321
 
4.9%
G 318
 
4.8%
T 304
 
4.6%
B 299
 
4.5%
Other values (16) 2638
40.1%

year
Real number (ℝ)

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.1614
Minimum2005
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:49.776249image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2006
Q12010
median2014
Q32018
95-th percentile2022
Maximum2022
Range17
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7187355
Coefficient of variation (CV)0.0023427792
Kurtosis-1.0716942
Mean2014.1614
Median Absolute Deviation (MAD)4
Skewness-0.076682854
Sum4429141
Variance22.266465
MonotonicityNot monotonic
2025-03-18T17:14:49.877192image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2017 147
 
6.7%
2011 146
 
6.6%
2014 144
 
6.5%
2019 143
 
6.5%
2015 142
 
6.5%
2012 141
 
6.4%
2018 141
 
6.4%
2016 141
 
6.4%
2013 136
 
6.2%
2010 124
 
5.6%
Other values (8) 794
36.1%
ValueCountFrequency (%)
2005 27
 
1.2%
2006 89
4.0%
2007 102
4.6%
2008 110
5.0%
2009 114
5.2%
2010 124
5.6%
2011 146
6.6%
2012 141
6.4%
2013 136
6.2%
2014 144
6.5%
ValueCountFrequency (%)
2022 114
5.2%
2021 122
5.5%
2020 116
5.3%
2019 143
6.5%
2018 141
6.4%
2017 147
6.7%
2016 141
6.4%
2015 142
6.5%
2014 144
6.5%
2013 136
6.2%

Happiness score
Real number (ℝ)

High correlation 

Distinct1713
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4792274
Minimum1.281
Maximum8.019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:49.998114image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1.281
5-th percentile3.6737
Q14.647
median5.432
Q36.3095
95-th percentile7.3765
Maximum8.019
Range6.738
Interquartile range (IQR)1.6625

Descriptive statistics

Standard deviation1.1255268
Coefficient of variation (CV)0.20541706
Kurtosis-0.5918227
Mean5.4792274
Median Absolute Deviation (MAD)0.825
Skewness-0.017831209
Sum12048.821
Variance1.2668105
MonotonicityNot monotonic
2025-03-18T17:14:50.133047image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.252 5
 
0.2%
4.64 4
 
0.2%
4.741 4
 
0.2%
5.304 4
 
0.2%
5.057 4
 
0.2%
5.887 4
 
0.2%
6.375 4
 
0.2%
3.476 3
 
0.1%
5.006 3
 
0.1%
4.683 3
 
0.1%
Other values (1703) 2161
98.3%
ValueCountFrequency (%)
1.281 1
< 0.1%
2.179 1
< 0.1%
2.352 1
< 0.1%
2.375 1
< 0.1%
2.436 1
< 0.1%
2.56 1
< 0.1%
2.634 1
< 0.1%
2.662 1
< 0.1%
2.688 1
< 0.1%
2.693 1
< 0.1%
ValueCountFrequency (%)
8.019 1
< 0.1%
7.971 1
< 0.1%
7.889 1
< 0.1%
7.858 1
< 0.1%
7.834 1
< 0.1%
7.794 1
< 0.1%
7.788 2
0.1%
7.78 1
< 0.1%
7.776 1
< 0.1%
7.771 1
< 0.1%

Log GDP per capita
Real number (ℝ)

High correlation 

Distinct1653
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.3897604
Minimum5.527
Maximum11.664
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:50.263962image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum5.527
5-th percentile7.3659
Q18.505
median9.492
Q310.366
95-th percentile10.9351
Maximum11.664
Range6.137
Interquartile range (IQR)1.861

Descriptive statistics

Standard deviation1.1481425
Coefficient of variation (CV)0.12227602
Kurtosis-0.75259262
Mean9.3897604
Median Absolute Deviation (MAD)0.933
Skewness-0.33667832
Sum20648.083
Variance1.3182312
MonotonicityNot monotonic
2025-03-18T17:14:50.768674image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.389760441 20
 
0.9%
8.902 5
 
0.2%
10.878 5
 
0.2%
9.383 5
 
0.2%
10.714 5
 
0.2%
9.381 5
 
0.2%
9.283 5
 
0.2%
9.813 4
 
0.2%
10.788 4
 
0.2%
8.067 4
 
0.2%
Other values (1643) 2137
97.2%
ValueCountFrequency (%)
5.527 1
< 0.1%
5.935 1
< 0.1%
5.943 1
< 0.1%
6.607 1
< 0.1%
6.687 1
< 0.1%
6.694 1
< 0.1%
6.699 1
< 0.1%
6.7 1
< 0.1%
6.707 1
< 0.1%
6.723 1
< 0.1%
ValueCountFrequency (%)
11.664 1
< 0.1%
11.66 1
< 0.1%
11.653 1
< 0.1%
11.649 1
< 0.1%
11.647 1
< 0.1%
11.645 1
< 0.1%
11.638 1
< 0.1%
11.637 1
< 0.1%
11.636 1
< 0.1%
11.635 1
< 0.1%

Social support
Real number (ℝ)

High correlation 

Distinct478
Distinct (%)21.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.81068115
Minimum0.228
Maximum0.987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:50.915590image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.228
5-th percentile0.5659
Q10.7475
median0.834
Q30.905
95-th percentile0.951
Maximum0.987
Range0.759
Interquartile range (IQR)0.1575

Descriptive statistics

Standard deviation0.12059451
Coefficient of variation (CV)0.14875702
Kurtosis1.1983974
Mean0.81068115
Median Absolute Deviation (MAD)0.075
Skewness-1.1220243
Sum1782.6879
Variance0.014543037
MonotonicityNot monotonic
2025-03-18T17:14:51.072500image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.937 17
 
0.8%
0.917 15
 
0.7%
0.818 15
 
0.7%
0.866 15
 
0.7%
0.878 15
 
0.7%
0.904 14
 
0.6%
0.909 14
 
0.6%
0.91 14
 
0.6%
0.863 14
 
0.6%
0.926 14
 
0.6%
Other values (468) 2052
93.3%
ValueCountFrequency (%)
0.228 1
< 0.1%
0.29 1
< 0.1%
0.291 2
0.1%
0.303 1
< 0.1%
0.32 1
< 0.1%
0.326 1
< 0.1%
0.366 1
< 0.1%
0.373 1
< 0.1%
0.382 1
< 0.1%
0.387 1
< 0.1%
ValueCountFrequency (%)
0.987 1
< 0.1%
0.985 2
0.1%
0.984 1
< 0.1%
0.983 2
0.1%
0.982 2
0.1%
0.98 2
0.1%
0.979 2
0.1%
0.977 2
0.1%
0.976 1
< 0.1%
0.975 1
< 0.1%

Healthy life expectancy at birth
Real number (ℝ)

High correlation 

Distinct1108
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.294582
Minimum6.72
Maximum74.475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:51.209430image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum6.72
5-th percentile51.014
Q159.4125
median64.88
Q368.33
95-th percentile71.6
Maximum74.475
Range67.755
Interquartile range (IQR)8.9175

Descriptive statistics

Standard deviation6.8158049
Coefficient of variation (CV)0.10768386
Kurtosis3.1403054
Mean63.294582
Median Absolute Deviation (MAD)4.22
Skewness-1.1597628
Sum139184.79
Variance46.455196
MonotonicityNot monotonic
2025-03-18T17:14:51.340356image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63.29458228 54
 
2.5%
66.6 16
 
0.7%
70 16
 
0.7%
65.8 16
 
0.7%
67 12
 
0.5%
67.5 12
 
0.5%
70.9 10
 
0.5%
65.7 10
 
0.5%
67.6 10
 
0.5%
66.3 9
 
0.4%
Other values (1098) 2034
92.5%
ValueCountFrequency (%)
6.72 1
< 0.1%
17.36 1
< 0.1%
28 1
< 0.1%
33.32 1
< 0.1%
38.64 1
< 0.1%
40.4 1
< 0.1%
41.48 1
< 0.1%
41.52 1
< 0.1%
41.6 1
< 0.1%
42.25 1
< 0.1%
ValueCountFrequency (%)
74.475 1
< 0.1%
74.35 1
< 0.1%
74.225 1
< 0.1%
74.1 1
< 0.1%
73.975 1
< 0.1%
73.925 1
< 0.1%
73.85 1
< 0.1%
73.8 1
< 0.1%
73.725 1
< 0.1%
73.65 1
< 0.1%

Freedom to make life choices
Real number (ℝ)

High correlation 

Distinct545
Distinct (%)24.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74784718
Minimum0.258
Maximum0.985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:51.470281image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.258
5-th percentile0.4838
Q10.659
median0.767
Q30.858
95-th percentile0.935
Maximum0.985
Range0.727
Interquartile range (IQR)0.199

Descriptive statistics

Standard deviation0.13908148
Coefficient of variation (CV)0.1859758
Kurtosis0.010315384
Mean0.74784718
Median Absolute Deviation (MAD)0.099
Skewness-0.67567438
Sum1644.516
Variance0.019343658
MonotonicityNot monotonic
2025-03-18T17:14:51.606204image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7478471837 33
 
1.5%
0.838 13
 
0.6%
0.891 11
 
0.5%
0.817 11
 
0.5%
0.905 11
 
0.5%
0.882 10
 
0.5%
0.733 10
 
0.5%
0.904 10
 
0.5%
0.878 10
 
0.5%
0.773 10
 
0.5%
Other values (535) 2070
94.1%
ValueCountFrequency (%)
0.258 1
< 0.1%
0.26 1
< 0.1%
0.281 1
< 0.1%
0.287 1
< 0.1%
0.295 1
< 0.1%
0.304 1
< 0.1%
0.306 1
< 0.1%
0.315 1
< 0.1%
0.332 1
< 0.1%
0.333 1
< 0.1%
ValueCountFrequency (%)
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.98 1
 
< 0.1%
0.975 1
 
< 0.1%
0.971 1
 
< 0.1%
0.97 3
0.1%
0.969 1
 
< 0.1%
0.968 1
 
< 0.1%
0.965 2
0.1%
0.964 2
0.1%

Generosity
Real number (ℝ)

High correlation  Missing 

Distinct625
Distinct (%)29.4%
Missing73
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean9.1251176 × 10-5
Minimum-0.338
Maximum0.703
Zeros7
Zeros (%)0.3%
Negative1187
Negative (%)54.0%
Memory size17.3 KiB
2025-03-18T17:14:51.744125image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-0.338
5-th percentile-0.229
Q1-0.112
median-0.023
Q30.092
95-th percentile0.29775
Maximum0.703
Range1.041
Interquartile range (IQR)0.204

Descriptive statistics

Standard deviation0.16107902
Coefficient of variation (CV)1765.2268
Kurtosis0.83127602
Mean9.1251176 × 10-5
Median Absolute Deviation (MAD)0.102
Skewness0.77703862
Sum0.194
Variance0.025946452
MonotonicityNot monotonic
2025-03-18T17:14:51.880049image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.027 11
 
0.5%
-0.063 11
 
0.5%
-0.019 11
 
0.5%
-0.023 10
 
0.5%
-0.069 10
 
0.5%
0.05 10
 
0.5%
-0.04 10
 
0.5%
-0.011 10
 
0.5%
-0.047 10
 
0.5%
-0.081 10
 
0.5%
Other values (615) 2023
92.0%
(Missing) 73
 
3.3%
ValueCountFrequency (%)
-0.338 1
< 0.1%
-0.319 1
< 0.1%
-0.316 1
< 0.1%
-0.31 1
< 0.1%
-0.309 1
< 0.1%
-0.308 1
< 0.1%
-0.307 1
< 0.1%
-0.306 1
< 0.1%
-0.3 1
< 0.1%
-0.299 1
< 0.1%
ValueCountFrequency (%)
0.703 1
< 0.1%
0.695 1
< 0.1%
0.694 1
< 0.1%
0.683 1
< 0.1%
0.654 1
< 0.1%
0.649 1
< 0.1%
0.563 1
< 0.1%
0.552 1
< 0.1%
0.551 1
< 0.1%
0.543 1
< 0.1%

Perceptions of corruption
Real number (ℝ)

Distinct602
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74520835
Minimum0.035
Maximum0.983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:52.017968image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.035
5-th percentile0.3239
Q10.698
median0.791
Q30.866
95-th percentile0.9381
Maximum0.983
Range0.948
Interquartile range (IQR)0.168

Descriptive statistics

Standard deviation0.18086464
Coefficient of variation (CV)0.24270346
Kurtosis2.0818756
Mean0.74520835
Median Absolute Deviation (MAD)0.082
Skewness-1.5311967
Sum1638.7132
Variance0.032712019
MonotonicityNot monotonic
2025-03-18T17:14:52.154880image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7452083533 116
 
5.3%
0.844 16
 
0.7%
0.884 14
 
0.6%
0.755 13
 
0.6%
0.743 13
 
0.6%
0.868 13
 
0.6%
0.841 13
 
0.6%
0.848 12
 
0.5%
0.849 12
 
0.5%
0.863 12
 
0.5%
Other values (592) 1965
89.4%
ValueCountFrequency (%)
0.035 1
< 0.1%
0.047 1
< 0.1%
0.06 1
< 0.1%
0.064 1
< 0.1%
0.066 1
< 0.1%
0.07 1
< 0.1%
0.078 1
< 0.1%
0.081 1
< 0.1%
0.095 1
< 0.1%
0.097 1
< 0.1%
ValueCountFrequency (%)
0.983 2
0.1%
0.979 1
 
< 0.1%
0.977 2
0.1%
0.976 2
0.1%
0.974 1
 
< 0.1%
0.973 2
0.1%
0.97 2
0.1%
0.969 1
 
< 0.1%
0.968 3
0.1%
0.967 3
0.1%

Positive affect
Real number (ℝ)

High correlation 

Distinct436
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65214759
Minimum0.179
Maximum0.884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:52.281808image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.179
5-th percentile0.475
Q10.573
median0.662
Q30.737
95-th percentile0.803
Maximum0.884
Range0.705
Interquartile range (IQR)0.164

Descriptive statistics

Standard deviation0.10533274
Coefficient of variation (CV)0.16151673
Kurtosis-0.17672688
Mean0.65214759
Median Absolute Deviation (MAD)0.08
Skewness-0.43850962
Sum1434.0725
Variance0.011094987
MonotonicityNot monotonic
2025-03-18T17:14:52.420738image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6521475862 24
 
1.1%
0.718 15
 
0.7%
0.699 14
 
0.6%
0.689 13
 
0.6%
0.74 13
 
0.6%
0.742 12
 
0.5%
0.702 12
 
0.5%
0.583 12
 
0.5%
0.658 12
 
0.5%
0.717 12
 
0.5%
Other values (426) 2060
93.7%
ValueCountFrequency (%)
0.179 1
< 0.1%
0.206 1
< 0.1%
0.263 1
< 0.1%
0.297 1
< 0.1%
0.298 1
< 0.1%
0.308 1
< 0.1%
0.324 1
< 0.1%
0.332 1
< 0.1%
0.347 1
< 0.1%
0.351 1
< 0.1%
ValueCountFrequency (%)
0.884 1
 
< 0.1%
0.876 1
 
< 0.1%
0.874 1
 
< 0.1%
0.86 1
 
< 0.1%
0.853 1
 
< 0.1%
0.851 1
 
< 0.1%
0.849 1
 
< 0.1%
0.847 1
 
< 0.1%
0.844 1
 
< 0.1%
0.841 5
0.2%

Negative affect
Real number (ℝ)

Distinct391
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27149336
Minimum0.083
Maximum0.705
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-03-18T17:14:52.552663image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.083
5-th percentile0.151
Q10.208
median0.262
Q30.3225
95-th percentile0.43
Maximum0.705
Range0.622
Interquartile range (IQR)0.1145

Descriptive statistics

Standard deviation0.086554752
Coefficient of variation (CV)0.31880983
Kurtosis0.78741987
Mean0.27149336
Median Absolute Deviation (MAD)0.056
Skewness0.73342787
Sum597.01389
Variance0.007491725
MonotonicityNot monotonic
2025-03-18T17:14:52.688585image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.206 19
 
0.9%
0.2714933578 16
 
0.7%
0.232 16
 
0.7%
0.24 16
 
0.7%
0.26 15
 
0.7%
0.226 15
 
0.7%
0.218 15
 
0.7%
0.233 15
 
0.7%
0.276 15
 
0.7%
0.268 15
 
0.7%
Other values (381) 2042
92.9%
ValueCountFrequency (%)
0.083 2
0.1%
0.093 2
0.1%
0.094 1
 
< 0.1%
0.095 2
0.1%
0.1 1
 
< 0.1%
0.103 1
 
< 0.1%
0.106 1
 
< 0.1%
0.107 1
 
< 0.1%
0.108 3
0.1%
0.109 1
 
< 0.1%
ValueCountFrequency (%)
0.705 1
< 0.1%
0.643 1
< 0.1%
0.622 1
< 0.1%
0.607 1
< 0.1%
0.599 1
< 0.1%
0.591 1
< 0.1%
0.581 1
< 0.1%
0.576 1
< 0.1%
0.57 1
< 0.1%
0.569 1
< 0.1%

Generositys
Real number (ℝ)

High correlation 

Distinct626
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1251176 × 10-5
Minimum-0.338
Maximum0.703
Zeros7
Zeros (%)0.3%
Negative1187
Negative (%)54.0%
Memory size17.3 KiB
2025-03-18T17:14:52.825497image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-0.338
5-th percentile-0.228
Q1-0.107
median-0.017
Q30.087
95-th percentile0.296
Maximum0.703
Range1.041
Interquartile range (IQR)0.194

Descriptive statistics

Standard deviation0.15838156
Coefficient of variation (CV)1735.6659
Kurtosis0.96280622
Mean9.1251176 × 10-5
Median Absolute Deviation (MAD)0.098
Skewness0.790248
Sum0.20066134
Variance0.025084718
MonotonicityNot monotonic
2025-03-18T17:14:52.960420image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.125117592 × 10-573
 
3.3%
-0.027 11
 
0.5%
-0.063 11
 
0.5%
-0.019 11
 
0.5%
-0.023 10
 
0.5%
-0.069 10
 
0.5%
0.05 10
 
0.5%
-0.04 10
 
0.5%
-0.011 10
 
0.5%
-0.047 10
 
0.5%
Other values (616) 2033
92.5%
ValueCountFrequency (%)
-0.338 1
< 0.1%
-0.319 1
< 0.1%
-0.316 1
< 0.1%
-0.31 1
< 0.1%
-0.309 1
< 0.1%
-0.308 1
< 0.1%
-0.307 1
< 0.1%
-0.306 1
< 0.1%
-0.3 1
< 0.1%
-0.299 1
< 0.1%
ValueCountFrequency (%)
0.703 1
< 0.1%
0.695 1
< 0.1%
0.694 1
< 0.1%
0.683 1
< 0.1%
0.654 1
< 0.1%
0.649 1
< 0.1%
0.563 1
< 0.1%
0.552 1
< 0.1%
0.551 1
< 0.1%
0.543 1
< 0.1%

Interactions

2025-03-18T17:14:47.154737image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:34.440015image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.655321image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.860623image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.624623image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.832932image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.044230image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.274537image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:43.651750image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.778098image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.982409image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.255690image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:34.551951image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.765247image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.967572image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.740549image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.948866image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.159166image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.376479image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:43.758689image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.888043image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.090347image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.356632image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:34.662877image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.887179image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:37.729135image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.853484image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.056804image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.276098image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.473424image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:43.866616image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.994982image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.206283image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.448581image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:34.770818image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.992128image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:37.819084image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.961430image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.159736image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.380048image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.568361image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:43.964571image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.102911image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.309231image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.552513image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:34.896744image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.111051image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:37.924015image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.071359image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.307653image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.496973image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.670311image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.074510image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.235835image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.432153image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.650466image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.005692image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.224985image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.026954image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.185292image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.410594image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.605911image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.771245image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.176450image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.347772image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.545087image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.755405image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.121626image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.341928image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.130905image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.299238image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.522538image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.716846image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.880181image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.284380image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.465705image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.651037image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.846344image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.224567image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.441871image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.225851image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.400180image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.618474image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.848780image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.969141image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.378335image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.566646image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.747980image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.950284image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.332504image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.544811image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.325793image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.507118image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.724412image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:41.954720image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:43.361916image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.477278image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.670597image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.848914image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:48.048228image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.440434image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.656748image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.428727image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.622053image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.833361image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.065649image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:43.462848image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.580219image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.778534image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:46.956851image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:48.153170image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:35.551379image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:36.762677image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:38.528669image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:39.729992image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:40.940289image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:42.172596image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:43.558805image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:44.681161image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:45.884475image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-03-18T17:14:47.055796image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-03-18T17:14:53.062362image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Freedom to make life choicesGenerosityGenerositysHappiness scoreHealthy life expectancy at birthLog GDP per capitaNegative affectPerceptions of corruptionPositive affectSocial supportyear
Freedom to make life choices1.0000.3480.3420.5410.3970.398-0.253-0.4630.5760.4500.229
Generosity0.3481.0001.0000.1630.0220.005-0.087-0.2230.3060.0890.011
Generositys0.3421.0001.0000.1660.0250.012-0.086-0.2230.3010.0900.007
Happiness score0.5410.1630.1661.0000.7580.800-0.297-0.3300.5110.7580.068
Healthy life expectancy at birth0.3970.0220.0250.7581.0000.836-0.139-0.2370.2640.6470.148
Log GDP per capita0.3980.0050.0120.8000.8361.000-0.251-0.2760.2530.7170.081
Negative affect-0.253-0.087-0.086-0.297-0.139-0.2511.0000.206-0.282-0.4290.202
Perceptions of corruption-0.463-0.223-0.223-0.330-0.237-0.2760.2061.000-0.273-0.213-0.115
Positive affect0.5760.3060.3010.5110.2640.253-0.282-0.2731.0000.4110.030
Social support0.4500.0890.0900.7580.6470.717-0.429-0.2130.4111.000-0.016
year0.2290.0110.0070.0680.1480.0810.202-0.1150.030-0.0161.000

Missing values

2025-03-18T17:14:48.293097image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-18T17:14:48.435016image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Country nameIso alphayearHappiness scoreLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affectGenerositys
0AfghanistanAFG20083.727.350.4550.500.720.170.880.410.260.17
1AfghanistanAFG20094.407.510.5550.800.680.190.850.480.240.19
2AfghanistanAFG20104.767.610.5451.100.600.120.710.520.280.12
3AfghanistanAFG20113.837.580.5251.400.500.160.730.480.270.16
4AfghanistanAFG20123.787.660.5251.700.530.240.780.610.270.24
5AfghanistanAFG20133.577.680.4852.000.580.060.820.550.270.06
6AfghanistanAFG20143.137.670.5352.300.510.110.870.490.380.11
7AfghanistanAFG20153.987.650.5352.600.390.080.880.490.340.08
8AfghanistanAFG20164.227.650.5652.920.520.040.790.500.350.04
9AfghanistanAFG20172.667.650.4953.250.43-0.120.950.430.37-0.12
Country nameIso alphayearHappiness scoreLog GDP per capitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionPositive affectNegative affectGenerositys
2189ZimbabweZWE20134.697.750.8048.800.58-0.090.830.620.18-0.09
2190ZimbabweZWE20144.187.750.7750.000.64-0.060.820.660.24-0.06
2191ZimbabweZWE20153.707.750.7451.200.67-0.110.810.640.18-0.11
2192ZimbabweZWE20163.737.740.7751.670.73-0.080.720.690.21-0.08
2193ZimbabweZWE20173.647.750.7552.150.75-0.080.750.730.22-0.08
2194ZimbabweZWE20183.627.780.7852.620.76-0.050.840.660.21-0.05
2195ZimbabweZWE20192.697.700.7653.100.63-0.050.830.660.23-0.05
2196ZimbabweZWE20203.167.600.7253.580.640.010.790.660.350.01
2197ZimbabweZWE20213.157.660.6954.050.67-0.080.760.610.24-0.08
2198ZimbabweZWE20223.307.670.6754.520.65-0.070.750.640.19-0.07